An interpretable machine learning prognostic system for locoregionally advanced nasopharyngeal carcinoma based on tumor burden features
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Title
An interpretable machine learning prognostic system for locoregionally advanced nasopharyngeal carcinoma based on tumor burden features
Authors
Keywords
Machine learning, Tumor burden, Prognosis, Therapeutics, Nasopharyngeal carcinoma
Journal
ORAL ONCOLOGY
Volume 118, Issue -, Pages 105335
Publisher
Elsevier BV
Online
2021-05-21
DOI
10.1016/j.oraloncology.2021.105335
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- (2019) Andreas Maier et al. Zeitschrift fur Medizinische Physik
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- Artificial intelligence and deep learning – Radiology's next frontier?
- (2018) Ray Cody Mayo et al. CLINICAL IMAGING
- Big Data and Machine Learning in Health Care
- (2018) Andrew L. Beam et al. JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION
- Development and validation of a gene expression-based signature to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma: a retrospective, multicentre, cohort study
- (2018) Xin-Ran Tang et al. LANCET ONCOLOGY
- The value of detailed MR imaging report of primary tumor and lymph nodes on prognostic nomograms for nasopharyngeal carcinoma after intensity-modulated radiotherapy
- (2018) Yanzi Wan et al. RADIOTHERAPY AND ONCOLOGY
- Dermatologist-level classification of skin cancer with deep neural networks
- (2017) Andre Esteva et al. NATURE
- Outcomes of adding induction chemotherapy to concurrent chemoradiotherapy for stage T3N0-1 nasopharyngeal carcinoma: a propensity-matched study
- (2017) Xiao-Wen Lan et al. OncoTargets and Therapy
- Global trends in incidence and mortality of nasopharyngeal carcinoma
- (2016) Ling-Ling Tang et al. CANCER LETTERS
- A prognostic model predicts the risk of distant metastasis and death for patients with nasopharyngeal carcinoma based on pre-treatment interleukin 6 and clinical stage
- (2016) Liangru Ke et al. CLINICAL IMMUNOLOGY
- Global cancer statistics, 2012
- (2015) Lindsey A. Torre et al. CA-A CANCER JOURNAL FOR CLINICIANS
- Ten-year outcomes of a randomised trial for locoregionally advanced nasopharyngeal carcinoma: A single-institution experience from an endemic area
- (2015) Pei-Yu Huang et al. EUROPEAN JOURNAL OF CANCER
- Establishment and Validation of Prognostic Nomograms for Endemic Nasopharyngeal Carcinoma
- (2015) Lin-Quan Tang et al. JNCI-Journal of the National Cancer Institute
- Clinical decision support system for end-stage kidney disease risk estimation in IgA nephropathy patients
- (2015) Francesco Pesce et al. NEPHROLOGY DIALYSIS TRANSPLANTATION
- Establishment and Validation of Prognostic Nomograms for Endemic Nasopharyngeal Carcinoma
- (2015) Lin-Quan Tang et al. JNCI-Journal of the National Cancer Institute
- Prognostic value of tumor volume for patients with nasopharyngeal carcinoma treated with concurrent chemotherapy and intensity-modulated radiotherapy
- (2013) Zheng Wu et al. JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY
- Estimating a time-dependent concordance index for survival prediction models with covariate dependent censoring
- (2012) Thomas A. Gerds et al. STATISTICS IN MEDICINE
- Empowering induction therapy for locally advanced head and neck cancer
- (2010) A. Argiris et al. ANNALS OF ONCOLOGY
- Prognostic impact of magnetic resonance imaging-detected cranial nerve involvement in nasopharyngeal carcinoma
- (2009) Lizhi Liu et al. CANCER
- Re-Evaluation of 6th Edition of AJCC Staging System for Nasopharyngeal Carcinoma and Proposed Improvement Based on Magnetic Resonance Imaging
- (2009) Yan-Ping Mao et al. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
- Prognostic Impact of Primary Tumor Volume in Patients With Nasopharyngeal Carcinoma Treated by Definitive Radiation Therapy
- (2009) Chunying Shen et al. LARYNGOSCOPE
- Comparison of Logistic Regression and Artificial Neural Network Models in Breast Cancer Risk Estimation
- (2009) Turgay Ayer et al. RADIOGRAPHICS
- Retropharyngeal lymph node metastasis in nasopharyngeal carcinoma detected by magnetic resonance imaging
- (2008) Linglong Tang et al. CANCER
- The N Staging System in Nasopharyngeal Carcinoma with Radiation Therapy Oncology Group Guidelines for Lymph Node Levels Based on Magnetic Resonance Imaging
- (2008) Y.-P. Mao et al. CLINICAL CANCER RESEARCH
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